# FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation

arXiv: https://arxiv.org/abs/2011.09022 · HTML: https://mlsanity.com/paper/2011.09022/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 22 of 22)

- [Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation](https://mlsanity.com/paper/2109.05877/) (2021)
- [Lero: A Learning-to-Rank Query Optimizer](https://mlsanity.com/paper/2302.06873/) (2023)
- [FactorJoin: A New Cardinality Estimation Framework for Join Queries](https://mlsanity.com/paper/2212.05526/) (2022)
- [A Unified Transferable Model for ML-Enhanced DBMS](https://mlsanity.com/paper/2105.02418/) (2021)
- [PRICE: A Pretrained Model for Cross-Database Cardinality Estimation](https://mlsanity.com/paper/2406.01027/) (2024)
- [Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data](https://mlsanity.com/paper/2210.05508/) (2022)
- [Learning to be a Statistician: Learned Estimator for Number of Distinct Values](https://mlsanity.com/paper/2202.02800/) (2022)
- [Duet: efficient and scalable hybriD neUral rElation undersTanding](https://mlsanity.com/paper/2307.13494/) (2023)
- [Glue: Adaptively Merging Single Table Cardinality to Estimate Join Query Size](https://mlsanity.com/paper/2112.03458/) (2021)
- [GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints](https://mlsanity.com/paper/2411.04525/) (2024)
- [DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation](https://mlsanity.com/paper/2503.08994/) (2025)
- [Is it Bigger than a Breadbox: Efficient Cardinality Estimation for Real World Workloads](https://mlsanity.com/paper/2510.03386/) (2025)
- [Baihe: SysML Framework for AI-driven Databases](https://mlsanity.com/paper/2112.14460/) (2021)
- [Learned k-NN Distance Estimation](https://mlsanity.com/paper/2208.14210/) (2022)
- [Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators](https://mlsanity.com/paper/2303.18042/) (2023)
- [BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment](https://mlsanity.com/paper/2306.00845/) (2023)
- [ACE: A Cardinality Estimator for Set-Valued Queries](https://mlsanity.com/paper/2503.14929/) (2025)
- [Sketched Sum-Product Networks for Joins](https://mlsanity.com/paper/2506.14034/) (2025)
- [QCardEst/QCardCorr: Quantum Cardinality Estimation and Correction](https://mlsanity.com/paper/2509.08817/) (2025)
- [ZeroCard: Cardinality Estimation with Zero Dependence on Target Databases -- No Data, No Query, No Retraining](https://mlsanity.com/paper/2510.07983/) (2025)
- [CoLSE: A Lightweight and Robust Hybrid Learned Model for Single-Table Cardinality Estimation using Joint CDF](https://mlsanity.com/paper/2512.12624/) (2025)
- [MICRO: A Lightweight Middleware for Optimizing Cross-store Cross-model Graph-Relation Joins [Technical Report]](https://mlsanity.com/paper/2603.13835/) (2026)

## Abstract (arXiv)

Query optimizers rely on accurate cardinality estimation (CardEst) to produce good execution plans. The core problem of CardEst is how to model the rich joint distribution of attributes in an accurate and compact manner. Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates, or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation. In this paper, we propose FLAT, a CardEst method that is simultaneously fast in probability computation, lightweight in model size and accurate in estimation quality. The key idea of FLAT is a novel unsupervised graphical model, called FSPN. It utilizes both independent and conditional factorization to adaptively model different levels of attributes correlations, and thus dovetails their advantages. FLAT supports efficient online probability computation in near liner time on the underlying FSPN model, provides effective offline model construction and enables incremental model updates. It can estimate cardinality for both single table queries and multi table join queries. Extensive experimental study demonstrates the superiority of FLAT over existing CardEst methods on well known IMDB benchmarks: FLAT achieves 1 to 5 orders of magnitude better accuracy, 1 to 3 orders of magnitude faster probability computation speed and 1 to 2 orders of magnitude lower storage cost. We also integrate FLAT into Postgres to perform an end to end test. It improves the query execution time by 12.9% on the benchmark workload, which is very close to the optimal result 14.2% using the true cardinality.